Suman Lata Tripathi, PhD, is a professor at Lovely Professional University, Phagwara, India with more than seventeen years of experience in academics. She has published more than 74 research papers in refereed science journals and conferences, 14 books, and a book series in addition to 13 Indian patents and four copyrights. She has organized several workshops, summer internships, and expert lectures for students and has worked as a session chair, conference steering committee member, editorial board member, and peer reviewer in national and international journals and conferences. Mufti Mahmud, PhD, is an associate professor of Cognitive Computing in the Department of Computer Science at Nottingham Trent University, United Kingdom. Additionally, he serves as the coordinator of the Computer Science and Informatics Unit of Assessment in the Research Excellence Framework at the university and the deputy group leader of and the Cognitive Computing and Brain Informatics research group. He is also an active member of the Computing and Informatics Research Centre and Medical Technologies Innovation Facility. He is a fellow of the Higher Education Academy, a senior member of the Institute of Electrical and Electronics Engineering, the Association of Computing Machinery, and a professional member of the British Computer Society. C. Narmadha, PhD, is an associate professor and head of the Department of Electronics and Communication Engineering at the Periyar Maniammai Institute of Science and Technology and University, India. She has more than 19 years of academic experience and has published more than 15 research articles. Additionally, she has organized several workshops, summer internships, expert lectures, and research and innovation activities for students. She is a professional associated as a member of the Indian Society for Technical Education, Computer Society of India, and Broadcast Engineering Society. Her areas of expertise include wireless sensor networks, computer and communication systems, and signal processing. S. Albert Alexander, PhD, is the recipient of the prestigious Raman Research Fellowship from the University Grants Commission from the Government of India. He has 15 years of academic and research experience, has published 57 technical papers in national and international journals, and presented 53 papers at national and international conferences. Additionally, he has completed four Government of India-funded projects and is currently working on an additional four research projects. He is a member and in prestigious positions in various national and international forums and has been invited as a speaker for over 250 programs across India and the United States.

• Preface xxiAcknowledgements xxiii1 Introduction to AI Techniques for 6G Application 1Manoj Singh Adhikari, Raju Patel, Manoj Sindhwani and Shippu Sachdeva1.1 Introduction 21.2 Different Generation of Communication: From 1G to 6G 41.2.1 First Generation (1G) 41.2.2 Second Generation (2G) 51.2.3 Third Generation (3G) 51.2.4 Fourth Generation (4G) 51.2.5 Fifth Generation (5G) 51.2.6 Sixth Generation (6G) 51.3 Key Features and Requirements of 6G Networks 61.3.1 Faster Data Speeds 61.3.2 Ultra-Low Latency 61.3.3 Massive Capacity 71.3.4 Energy Efficiency 71.3.5 Seamless Connectivity 71.3.6 Advanced Spectrum Management 71.3.7 Enhanced Security and Privacy 71.3.8 Artificial Intelligence Integration 71.3.9 Heterogeneous Network Architecture 81.4 Role of Artificial Intelligence in 6G 81.4.1 Intelligent Radio Resource Management 91.4.2 Beamforming and MIMO 91.4.3 Intelligent Network Slicing 91.4.4 Intelligent Edge Computing 91.4.5 Intelligent Internet of Things 91.4.6 Enhanced Privacy 101.4.7 Intelligent Network Organization 101.4.8 Intelligent User Experience and Services 101.5 Machine Learning for 6G Networks 101.5.1 Intelligent Resource Management 111.5.2 Dynamic Spectrum Access 111.5.3 Intelligent Beamforming 111.5.4 Network Anomaly Detection 111.5.5 Intelligent Edge Computing 111.5.6 Intelligent Internet of Things 121.5.7 Intelligent Network Slicing 121.5.8 Intelligent Network Planning and Optimization 121.5.9 Predictive Maintenance 121.6 Deep Learning for 6G Applications 121.6.1 Enhanced Communication Systems 131.6.2 Intelligent Beamforming and Antenna Systems 131.6.3 Image and Video Processing 131.6.4 Intelligent Internet of Things 131.6.5 Autonomous Systems 131.6.6 Natural Language Processing and Speech Recognition 141.6.7 Augmented Reality and Virtual Reality 141.6.8 Network Security 141.7 Edge Computing and AI in 6G 141.7.1 Distributed Intelligence 141.7.2 Low-Latency Applications 151.7.3 Intelligent Edge Devices 151.7.4 Edge-AI-Assisted Network Management 151.7.5 Federated Learning 151.7.6 AI-Driven Security 151.7.7 Edge-AI for Content Delivery 161.7.8 Context-Aware Applications 161.8 AI-Enhanced Network Security in 6G 161.8.1 Threat Detection and Prevention 161.8.2 Anomaly Detection 171.8.3 Intrusion Detection and Prevention Systems (IDPS) 171.8.4 User Authentication 171.8.5 AI-Enabled Threat Intelligence 171.8.6 Automated Security Incident Response 171.8.7 AI-Enhanced Security Analytics 181.8.8 Privacy-Preserving Techniques 181.9 Quantum Computing and AI Fusion in 6G 181.9.1 Enhanced AI Algorithms 181.9.2 Optimization and Search Problems 191.9.3 Security and Encryption 191.9.4 Quantum-Assisted Machine Learning 191.9.5 Quantum Sensor Networks 191.9.6 Quantum-Assisted Simulation 191.9.7 Quantum Machine Learning 201.9.8 Quantum-Assisted Optimization 201.10 AI for Smart City Applications in 6G 201.10.1 Intelligent Traffic Management 201.10.2 Energy Management and Sustainability 211.10.3 Smart Infrastructure Monitoring 211.10.4 Waste Management 211.10.5 Smart Public Security and Safety 211.10.6 AI-Enabled Citizen Services 211.10.7 Urban Planning and Design 221.10.8 Data Analytics and Insights 221.11 Challenges and Future Directions 221.11.1 Technical Complexity 221.11.1.1 Future Directions 231.11.2 Privacy and Security 231.11.2.1 Future Directions 231.11.3 Ethical Considerations 241.11.3.1 Future Directions 241.11.4 Infrastructure and Energy Efficiency 241.11.4.1 Future Directions 241.11.5 Collaboration and Standardization 241.11.5.1 Future Directions 251.11.6 Socioeconomic Impact 251.11.6.1 Future Directions 251.11.7 Environmental Sustainability 251.11.7.1 Future Directions 251.12 Conclusion 25References 262 AI Techniques for 6G Applications 29Jyoti R. Munavalli, Rashmi R. Deshpande and Jayashree M. Oli2.1 6G Communication 302.2 Artificial Intelligence (AI) Computing in 6G 342.3 Role of AI in 6G 372.4 AI Techniques for 6G 382.4.1 Supervised Learning 392.4.2 Unsupervised Learning 412.4.3 Reinforcement Learning 422.4.4 Federated Learning 442.4.5 Deep Learning 462.5 Use Cases/Applications 472.5.1 Holographic Applications 472.5.2 Ubiquitous Computing 482.5.3 Deep Sensing/Tactile Internet 502.5.4 Dynamic Spectrum Allocation 512.6 Conclusion 53References 533 An Evaluation of Pervasive Computing Using Narrowband Technology: Exploring the Implications for 5G and Future Generations 57Sriharipriya K. C., Athira Soman Nair, Kannanpuzha Chelsea Antony, Megha Nair B. and Amala Ipe3.1 Introduction 583.2 Features 593.2.1 Power Consumption 593.2.2 Improved Coverage and Sensitivity with Low Latency 613.2.3 Transmission Mode 613.2.4 Resource of Spectrum 623.2.5 Mode of Working 623.2.6 Structure of Frame 643.2.7 Network of NB-IoT 643.2.8 Semi-Static Link Adaptation 663.2.9 Retransmission of Data 663.3 Basic Principles and Core Technologies of Narrowband 673.3.1 Theory of Analysis of Connection 673.3.2 Theory of Latency Survey 683.3.3 The Mechanism for Coverage Enhancement 693.3.4 Technology with Ultra-Low Power 703.3.5 Relationship of Coupling Between Signaling and Data 713.4 Correlation of Other Communication Technology with NB-IoT 723.4.1 With eMTC Technology 723.4.1.1 Coverage 743.4.1.2 Power Consumption 753.4.1.3 Connection Count 753.4.1.4 Voice Assistance 763.4.1.5 Mobility Management 763.4.1.6 Network Deployment’s Effect on the Current Network 763.4.1.7 Operative Mode 773.4.1.8 Combined Results 773.4.2 With More Wireless Network Methods 773.5 Applications 803.6 Security Needs 833.6.1 Perception Layer 843.6.2 Transmission Layers 853.6.3 Application Layer 863.7 Conclusion 87References 884 Cumulant-Based Performance Analysis of 5G and 6G Communication Networks 93Madhusmita Mishra, Sarat Kumar Patra and Ashok Kumar Turuk4.1 Introduction 944.2 Performance Analysis of the Modified BSLM Technique Using PAPR Characteristics and Various Phase Sequences 964.2.1 Overview of SLM-Based PAPR Reduction and Modification 964.2.2 PAPR Reduction Analysis Using CCDF 1004.2.3 Analysis of PAPR Reduction Using Various Phase Sequences 1014.3 Mutual Independency Basing on Joint Cumulants 1084.4 Computational Complexity 1104.5 Conclusion 110References 1115 Leveraging 6G Networks for Disaster Monitoring and Management in Remote Sensing 115G. Vinuja and N. Bharatha Devi5.1 Introduction 1165.2 Literature Review 1185.2.1 Overview of 6G Networks and Their Potential Benefits in Disaster Management 1275.3 Real-Time Disaster Monitoring and Management Using Remote Technologies 1285.3.1 Enhanced Connectivity 1285.3.2 Remote Sensing and Monitoring 1285.3.3 Data Analytics and AI 1295.3.4 Virtual Reality (VR) and Augmented Reality (AR) 1295.3.5 Telemedicine and Remote Healthcare 1295.3.6 Public Awareness and Communication 1295.3.7 Smart Infrastructure and IoT Integration 1305.3.8 Quicker Response Times 1305.3.9 Enhanced Risk Assessment 1305.3.10 Resource Allocation Optimization 1305.3.11 Enhanced Coordination and Collaboration 1305.3.12 Targeted Recovery and Reconstruction 1315.3.13 Enhanced Preparedness and Planning 1315.4 Methodology 1315.4.1 Description of Research Design 1325.4.2 Data Collection Methods 1335.4.3 Analysis Techniques 1345.5 Results 1345.5.1 Summary of Data Collected 1355.5.2 Analysis of Data 1365.5.3 Discussion of Findings 1365.6 Discussion 1395.6.1 Interpretation of Results 1395.6.2 Implications for the Future of Disaster Management 1405.7 Conclusion 140References 1416 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring 145G. Vinuja and N. Bharatha Devi6.1 Introduction 1456.2 Literature Review 1496.3 Experimental Methods and Materials 1536.3.1 Fast Data Transfer and Processing 1536.3.2 Improved Accuracy and Precision in Monitoring 1546.3.3 Enhanced Data Security and Privacy 1556.4 Results and Discussion 1566.4.1 Innovative Remote Sensing Devices 1566.4.2 Real-Time Monitoring Using Smart Sensors 1576.4.3 Integration of 6G Technology and Artificial Intelligence 1596.5 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring 1596.5.1 Soil and Water Quality Monitoring 1606.5.2 Climate and Weather Monitoring 1606.5.3 Air Pollution Monitoring 1616.6 Challenges and Limitations of Implementing 6G Technology in Environmental Monitoring 1616.6.1 High Cost of Installation and Maintenance 1626.6.2 Lack of Trained Professionals in 6G Technology 1626.6.3 Ethical and Legal Concerns Surrounding Data Privacy 1636.7 Conclusion 163References 1647 Transforming Remote Sensing with Sixth-Generation Wireless Technology 169Bishnu Kant Shukla, Amit Tripathi, Ayushi Bhati, Vaishnavi Bansal, Pushpendra Kumar Sharma and Shivam Verma7.1 Introduction 1707.2 Understanding Remote Sensing 1717.2.1 Scattering and Absorption of EMR in Atmosphere 1717.2.2 Interaction of EMR with Target 1727.2.3 Spectral Signatures of Different Targets 1727.3 Sensor Technologies in Remote Sensing 1737.3.1 Passive and Active Sensors 1737.3.2 Hyperspectral and Multispectral Sensors 1737.3.3 Thermal Imaging 1747.3.4 Geostationary and Geosynchronous Satellites 1757.4 Resolution in Remote Sensing 1767.4.1 Spatial Resolution 1767.4.2 Spectral Resolution 1777.4.3 Temporal Resolution 1787.4.4 Radiometric Resolution 1787.5 Remote Sensing Techniques and Processing 1797.5.1 False Color Composite, True Color Composite 1797.5.2 Stereoscopy 1797.5.3 Along-Track Scanners, Across-Track Scanners 1807.5.4 Instantaneous Field of View (IFOV) 1807.5.5 Digital Image Processing 1817.6 Microwave Remote Sensing 1827.6.1 Radar 1837.6.2 Radar Shadow Effects, Layover Effects 1837.7 The Advent of 6G Technology 1847.7.1 Understanding 6G Technology 1847.7.2 Potential Impact of 6G on Remote Sensing 1857.8 Transforming Remote Sensing with 6G 1867.8.1 Improved Data Transfer and Processing 1867.8.2 Energy Efficiency in Remote Sensing Systems 1877.8.3 Increased Device Connectivity 1887.9 Case Studies: Application of 6G in Remote Sensing 1907.9.1 Agriculture: Crop Type Mapping, Crop Monitoring, and Damage Assessment 1907.9.2 Forestry: Species Identification and Typing, Burn Mapping 1927.9.3 Geology 1927.10 Conclusion 193References 1958 Deep Learning Models for Image Annotation Application in a 6G Network Environment 201Sandhya Avasthi, Suman Lata Tripathi, Tanushree Sanwal and Mufti Mahmud8.1 Introduction 2028.1.1 Image Detection and Annoation Applications 2038.1.2 How Do 6G Networks Enhance Image Annotation Performance? 2048.2 6G Network Overview 2058.2.1 5G Limitations 2068.2.2 Deep Learning with 6G 2078.3 Deep Learning Models for Image Annotation 2078.3.1 Convolution Neural Network (CNN) 2088.3.2 Recurrent Neural Network 2098.3.3 Long Short-Term Memory (LSTM) 2108.4 Automatic Image Annotation Framework in Real Time 2118.4.1 Deep Learning-Based Image Annotation Process Pipeline 2118.4.2 Preprocessing 2118.4.3 Feature Extraction 2128.4.4 Segmentation 2138.4.5 Object Detection 2148.4.6 Annotation or Labeling of Objects 2148.5 Challenges in Implementing Image Annotation Application 2148.6 6G and Transformation World Wide 2158.7 Challenges in 6G 2168.8 Conclusion 218References 2199 Integration of Artificial Intelligence in 6G Networks for Processing Blood Cancer Data 223R. Senthil Ganesh, S. A. Sivakumar and B. Maruthi Shankar9.1 Insights into 6G Networks: Revolutionizing Healthcare Data Processing 2249.2 Methodology for Blood Cancer Data Processing 2269.3 Enhancing Diagnostics, Treatment Planning, and Patient Monitoring Using 6G Networks 2289.4 Various AI Techniques for Analyzing Blood Cancer Data 2299.5 AI Integration in 6G Networks for Blood Cancer Data Processing 2309.6 Results and Discussions 2339.7 Conclusion 236References 23810 Enhancing Connectivity and Data-Driven Decision-Making for Smart Agriculture by Embracing 6G Technology 241Y.V.R. Naga Pawan and Kolla Bhanu Prakash10.1 Fundamental Concepts of Smart Agriculture 24210.1.1 Smart Agriculture 24210.2 Applications of 6G in SA 24310.3 Empowerment of 6G in SA 24910.4 Enhanced Monitoring and Predictive Analytics in SA 25010.4.1 Predictive Analytics 25210.5 Advantages of 6G in SA 25310.6 Challenges in the Implementation of 6G in SA 257References 26011 Security and Cost Optimization in Laser-Based Fencing Solutions 265Sanmukh Kaur and Anurupa Lubana11.1 Introduction 26511.2 Potential Security Challenges 26611.2.1 Beam Spoofing 26611.2.2 Beam Bending 26811.3 Objectives of the Chapter 26811.3.1 To Defend the Laser Fencing Against Potential Attacks 26811.3.2 To Optimize the Cost of Manufacturing and Operating 26811.4 Secure Communication Protocol 26911.4.1 Node Setup 26911.4.2 Protocol 27011.4.2.1 Packet Structure 27011.4.2.2 Fence State 27011.4.2.3 Seed and Encryption 27111.4.2.4 Timestamp Counter 27111.4.2.5 Error Checking 27111.5 Algorithm 27211.6 Conclusion 275References 27612 Security and Privacy in 6G-Based Human–Computer Interfaces: Challenges and Opportunities 277Kamaraj Arunachalam and Senthil Kumar Jagatheesaperumal12.1 Introduction 27812.2 Evolution of 6G Networks and HCIs 28012.2.1 Connected Robotics and Autonomous Systems 28112.2.2 Wireless Brain–Computer Interactions (BCIs) 28112.2.3 Haptic Communication and Smart Healthcare 28212.2.4 Automation and Industrial Ecosystem 28212.2.5 Internet of Everything (IoE) 28212.3 Risks and Vulnerabilities in 6G-Based HCIs 28312.4 Solutions and Strategies for Ensuring Security and Privacy 28612.4.1 Authentication Techniques in 6G HCIs 28612.4.2 Encryption Algorithms and Protocols 28712.4.3 Cybersecurity Measures for HCIs 28812.4.4 Privacy-Enhancing Technologies 28912.5 Future Trends and Opportunities for Enhancing Security and Privacy 29112.5.1 Advancements in User Identification and Authentication 29112.5.2 Secure Data Transmission and Storage 29212.5.3 Incorporating Privacy by Design 29312.5.4 Collaboration and Standardization Efforts 29312.6 Conclusion 294References 29413 Security and Privacy in 6G Applications: Optimization and Realization of Stochastic-Based Rapid Random Number Generation 299S. Nithya Devi, S. Senthil Kumar, V. K. Reshma and S. Shanmugaraju13.1 Introduction 30013.2 Literature Review 30213.3 Problem with Sensor Data 30413.4 Study Process 30513.4.1 Conventional Digital Clock Manager Scheme 30513.4.2 Stochastic Circuits 30713.4.3 Rapid Generating of Random Numbers Using a Stochastic Model 30713.4.4 Received Signal Strength Indicator (RSSI) 30913.4.5 Setting Up the Experiment and Collecting Data 31013.4.6 QCA Multiplexers and D-Latch 31013.5 Results and Analysis 31213.6 Conclusion 315References 31614 Roles and Challenges of 6G for the Human–Computer Interface 319Priyabrata Dash, Akankshya Patnaik, Sarat Kumar Sahoo and Franco Fernando Yanine14.1 Introduction 32014.2 Sixth Generation 32214.3 Roles of 6G for the Human–Computer Interface 32614.4 Challenges of 6G for the Human–Computer Interface 32814.5 Uses of 6G in Different Sectors 33114.6 Impact of 6G in Organizations 33314.7 Conclusion 334References 33515 Leveraging 6G Technology for Advancements in Smart Agriculture: Opportunities and Challenges 339B. Sathyasri, R.S. Valarmathi and G. Aloy Anuja Mary15.1 Introduction 34015.2 Literature Review 34515.3 Methodology 34515.3.1 Benefits of 6G in Smart Agriculture 34515.3.2 Increased Precision and Accuracy in Farming Practices 34615.3.3 Real-Time Monitoring and Data Collection 34615.3.4 Improved Communication and Collaboration Among Farmers 34715.3.5 Efficient Allocation of Resources 34715.3.6 Enhanced Crop Yields and Quality 34715.4 Challenges to Implementing 6G in Smart Agriculture 34815.4.1 High Cost of Technology 34815.4.2 Limited Network Coverage in Rural Areas 34915.4.3 Concerns over Data Security and Privacy 34915.4.4 Need for Technical Expertise to Operate and Maintain Technology 35015.5 Potential Applications of 6G in Smart Agriculture 35015.5.1 Crop Monitoring and Management 35115.5.2 Livestock Monitoring and Disease Control 35115.5.3 Smart Irrigation Systems 35215.5.4 Automated Machinery and Equipment 35215.5.5 Supply Chain Management 35315.6 Expected Outcomes 35315.7 Example of a Farm or Company That Has Successfully Adopted 6G Technology 35415.8 Benefits Experienced and Impact on Agricultural Productivity 35515.8.1 Lessons Learned and Recommendations for Others 35615.9 Conclusion 358References 35916 Exploring 6G Research: Advancements, Applications, and Challenges 363S. Senthil Kumar, S. Balaji, S. Nithya Devi and V. Priyadharsini16.1 Introduction 36416.2 Our Contributions and Comparable Work 36516.2.1 Previous Studies 36616.2.2 Contributions 36716.3 Credibility 36716.3.1 Reliability 36716.3.2 Security and Safety 36816.3.3 Dependability in 6G Networks 36816.4 Reliability, ML, and 6G 36816.4.1 Background in Brief 36916.4.2 Dependability of Federated Learning 36916.4.2.1 Reliability 37016.4.2.2 Availability 37116.4.2.3 Safety 37116.5 Dependability for Mission-Critical Applications 37116.5.1 Dependability Analysis of 6G MCAs 37216.5.2 Availability 37216.6 Future Research Directions 37216.7 Conclusions 374References 37417 E-Travel ID-Based Bus Fare Collection System Using 6G Networks 379S. A. Sivakumar, Pavithra K., Pavatharani P., Naviyarasu G. and Sajetha M.17.1 Insights into 6G Networks 38017.2 Impact of 6G on Transportation Sector 38117.3 Existing Approach and Problem Identification 38317.4 E-Travel ID-Based Bus Fare Collection System Using 6G Networks 38517.5 Results and Discussion 38817.6 Conclusion 392References 39318 Alert Generation Tool for Messaging Systems 395Akshaya K. and Sanmukh Kaur18.1 Introduction 39518.2 Importance of Alerts in the Messaging System 39618.2.1 System Health Monitoring 39618.2.2 Proactive Issue Resolution 39718.2.3 Performance Optimization 39718.2.4 Capacity Planning 39718.2.5 Security and Compliance 39718.3 Monitoring CPU Usage in Real Time 39818.3.1 Importance of CPU Usage Monitoring 39818.3.1.1 Identifying Performance Bottlenecks 39818.3.1.2 Diagnosing Performance Issues 39818.3.1.3 Optimizing Resource Allocation 39818.3.1.4 Proactive Issue Detection 39918.3.1.5 Capacity Planning and Scaling 39918.3.1.6 Resource Efficiency and Cost Optimization 39918.3.2 Methodology 39918.3.2.1 Importing the Necessary Libraries 39918.3.2.2 User Input for Process ID 39918.3.2.3 Defining the “warning()” Function 40018.3.2.4 Defining the “monitor()” Function 40018.3.2.5 Scheduling the Monitoring Tasks 40018.3.2.6 Running the Monitoring Loop 40118.3.2.7 Python Code 40118.3.3 Output 40318.3.4 Benefits of Real-Time CPU Usage Monitoring 40418.4 URL Tracking 40418.4.1 Methodology 40518.4.1.1 Python Code 40618.4.1.2 Output 40618.4.1.3 Python Code 40718.4.2 Output 40818.5 Automated Delivery Performance Monitoring 40918.5.1 Methodology 41018.5.1.1 Code 41218.5.2 Output 41318.5.3 Applications 41518.5.3.1 Marketing Campaigns 41518.5.3.2 Transactional Notifications 41518.5.3.3 Customer Support Systems 41518.5.3.4 System Alerts 41518.5.3.5 Performance Evaluation 41518.6 High Volume of Testing Message Alert 41618.6.1 Methodology 41618.6.1.1 Import Necessary Libraries 41618.6.1.2 Set Up Twilio and Email Credentials 41618.6.1.3 Establish a Connection to MySQL Database 41618.6.1.4 Create a Cursor Object and Execute a Query 41618.6.1.5 Fetch Data and Create a Pandas DataFrame 41718.6.1.6 Export Data to Excel 41718.6.1.7 Count the Number of Testing Messages 41718.6.1.8 Close the Cursor and Connection 41718.6.1.9 Print Status Messages 41718.6.1.10 Send SMS and Email Notifications 41718.6.1.11 Python Code 41818.6.2 Output 41918.7 Conclusion 421References 42119 Design of an Underwater Robotic Fish Controlled through a Mobile Phone 423Mohammed Nisam, N. Mouli Sharm, Vajid N. O., Sobhit Saxena and Suman Lata Tripathi19.1 Introduction 42319.1.1 Block Diagram 42519.1.2 Flowchart and Explanation 42719.2 Module Code Description 42719.3 Description of Proposed Robotic Fish 42919.4 Component and Material Selection 43019.5 Conclusion 43519.6 Suggestion for Future Work 435References 436About the Editors 439Index 441

Suman Lata Tripathi, PhD, is a professor at Lovely Professional University, Phagwara, India with more than seventeen years of experience in academics. She has published more than 74 research papers in refereed science journals and conferences, 14 books, and a book series in addition to 13 Indian patents and four copyrights. She has organized several workshops, summer internships, and expert lectures for students and has worked as a session chair, conference steering committee member, editorial board member, and peer reviewer in national and international journals and conferences. Mufti Mahmud, PhD, is an associate professor of Cognitive Computing in the Department of Computer Science at Nottingham Trent University, United Kingdom. Additionally, he serves as the coordinator of the Computer Science and Informatics Unit of Assessment in the Research Excellence Framework at the university and the deputy group leader of and the Cognitive Computing and Brain Informatics research group. He is also an active member of the Computing and Informatics Research Centre and Medical Technologies Innovation Facility. He is a fellow of the Higher Education Academy, a senior member of the Institute of Electrical and Electronics Engineering, the Association of Computing Machinery, and a professional member of the British Computer Society. C. Narmadha, PhD, is an associate professor and head of the Department of Electronics and Communication Engineering at the Periyar Maniammai Institute of Science and Technology and University, India. She has more than 19 years of academic experience and has published more than 15 research articles. Additionally, she has organized several workshops, summer internships, expert lectures, and research and innovation activities for students. She is a professional associated as a member of the Indian Society for Technical Education, Computer Society of India, and Broadcast Engineering Society. Her areas of expertise include wireless sensor networks, computer and communication systems, and signal processing. S. Albert Alexander, PhD, is the recipient of the prestigious Raman Research Fellowship from the University Grants Commission from the Government of India. He has 15 years of academic and research experience, has published 57 technical papers in national and international journals, and presented 53 papers at national and international conferences. Additionally, he has completed four Government of India-funded projects and is currently working on an additional four research projects. He is a member and in prestigious positions in various national and international forums and has been invited as a speaker for over 250 programs across India and the United States.

• Preface xxiAcknowledgements xxiii1 Introduction to AI Techniques for 6G Application 1Manoj Singh Adhikari, Raju Patel, Manoj Sindhwani and Shippu Sachdeva1.1 Introduction 21.2 Different Generation of Communication: From 1G to 6G 41.2.1 First Generation (1G) 41.2.2 Second Generation (2G) 51.2.3 Third Generation (3G) 51.2.4 Fourth Generation (4G) 51.2.5 Fifth Generation (5G) 51.2.6 Sixth Generation (6G) 51.3 Key Features and Requirements of 6G Networks 61.3.1 Faster Data Speeds 61.3.2 Ultra-Low Latency 61.3.3 Massive Capacity 71.3.4 Energy Efficiency 71.3.5 Seamless Connectivity 71.3.6 Advanced Spectrum Management 71.3.7 Enhanced Security and Privacy 71.3.8 Artificial Intelligence Integration 71.3.9 Heterogeneous Network Architecture 81.4 Role of Artificial Intelligence in 6G 81.4.1 Intelligent Radio Resource Management 91.4.2 Beamforming and MIMO 91.4.3 Intelligent Network Slicing 91.4.4 Intelligent Edge Computing 91.4.5 Intelligent Internet of Things 91.4.6 Enhanced Privacy 101.4.7 Intelligent Network Organization 101.4.8 Intelligent User Experience and Services 101.5 Machine Learning for 6G Networks 101.5.1 Intelligent Resource Management 111.5.2 Dynamic Spectrum Access 111.5.3 Intelligent Beamforming 111.5.4 Network Anomaly Detection 111.5.5 Intelligent Edge Computing 111.5.6 Intelligent Internet of Things 121.5.7 Intelligent Network Slicing 121.5.8 Intelligent Network Planning and Optimization 121.5.9 Predictive Maintenance 121.6 Deep Learning for 6G Applications 121.6.1 Enhanced Communication Systems 131.6.2 Intelligent Beamforming and Antenna Systems 131.6.3 Image and Video Processing 131.6.4 Intelligent Internet of Things 131.6.5 Autonomous Systems 131.6.6 Natural Language Processing and Speech Recognition 141.6.7 Augmented Reality and Virtual Reality 141.6.8 Network Security 141.7 Edge Computing and AI in 6G 141.7.1 Distributed Intelligence 141.7.2 Low-Latency Applications 151.7.3 Intelligent Edge Devices 151.7.4 Edge-AI-Assisted Network Management 151.7.5 Federated Learning 151.7.6 AI-Driven Security 151.7.7 Edge-AI for Content Delivery 161.7.8 Context-Aware Applications 161.8 AI-Enhanced Network Security in 6G 161.8.1 Threat Detection and Prevention 161.8.2 Anomaly Detection 171.8.3 Intrusion Detection and Prevention Systems (IDPS) 171.8.4 User Authentication 171.8.5 AI-Enabled Threat Intelligence 171.8.6 Automated Security Incident Response 171.8.7 AI-Enhanced Security Analytics 181.8.8 Privacy-Preserving Techniques 181.9 Quantum Computing and AI Fusion in 6G 181.9.1 Enhanced AI Algorithms 181.9.2 Optimization and Search Problems 191.9.3 Security and Encryption 191.9.4 Quantum-Assisted Machine Learning 191.9.5 Quantum Sensor Networks 191.9.6 Quantum-Assisted Simulation 191.9.7 Quantum Machine Learning 201.9.8 Quantum-Assisted Optimization 201.10 AI for Smart City Applications in 6G 201.10.1 Intelligent Traffic Management 201.10.2 Energy Management and Sustainability 211.10.3 Smart Infrastructure Monitoring 211.10.4 Waste Management 211.10.5 Smart Public Security and Safety 211.10.6 AI-Enabled Citizen Services 211.10.7 Urban Planning and Design 221.10.8 Data Analytics and Insights 221.11 Challenges and Future Directions 221.11.1 Technical Complexity 221.11.1.1 Future Directions 231.11.2 Privacy and Security 231.11.2.1 Future Directions 231.11.3 Ethical Considerations 241.11.3.1 Future Directions 241.11.4 Infrastructure and Energy Efficiency 241.11.4.1 Future Directions 241.11.5 Collaboration and Standardization 241.11.5.1 Future Directions 251.11.6 Socioeconomic Impact 251.11.6.1 Future Directions 251.11.7 Environmental Sustainability 251.11.7.1 Future Directions 251.12 Conclusion 25References 262 AI Techniques for 6G Applications 29Jyoti R. Munavalli, Rashmi R. Deshpande and Jayashree M. Oli2.1 6G Communication 302.2 Artificial Intelligence (AI) Computing in 6G 342.3 Role of AI in 6G 372.4 AI Techniques for 6G 382.4.1 Supervised Learning 392.4.2 Unsupervised Learning 412.4.3 Reinforcement Learning 422.4.4 Federated Learning 442.4.5 Deep Learning 462.5 Use Cases/Applications 472.5.1 Holographic Applications 472.5.2 Ubiquitous Computing 482.5.3 Deep Sensing/Tactile Internet 502.5.4 Dynamic Spectrum Allocation 512.6 Conclusion 53References 533 An Evaluation of Pervasive Computing Using Narrowband Technology: Exploring the Implications for 5G and Future Generations 57Sriharipriya K. C., Athira Soman Nair, Kannanpuzha Chelsea Antony, Megha Nair B. and Amala Ipe3.1 Introduction 583.2 Features 593.2.1 Power Consumption 593.2.2 Improved Coverage and Sensitivity with Low Latency 613.2.3 Transmission Mode 613.2.4 Resource of Spectrum 623.2.5 Mode of Working 623.2.6 Structure of Frame 643.2.7 Network of NB-IoT 643.2.8 Semi-Static Link Adaptation 663.2.9 Retransmission of Data 663.3 Basic Principles and Core Technologies of Narrowband 673.3.1 Theory of Analysis of Connection 673.3.2 Theory of Latency Survey 683.3.3 The Mechanism for Coverage Enhancement 693.3.4 Technology with Ultra-Low Power 703.3.5 Relationship of Coupling Between Signaling and Data 713.4 Correlation of Other Communication Technology with NB-IoT 723.4.1 With eMTC Technology 723.4.1.1 Coverage 743.4.1.2 Power Consumption 753.4.1.3 Connection Count 753.4.1.4 Voice Assistance 763.4.1.5 Mobility Management 763.4.1.6 Network Deployment’s Effect on the Current Network 763.4.1.7 Operative Mode 773.4.1.8 Combined Results 773.4.2 With More Wireless Network Methods 773.5 Applications 803.6 Security Needs 833.6.1 Perception Layer 843.6.2 Transmission Layers 853.6.3 Application Layer 863.7 Conclusion 87References 884 Cumulant-Based Performance Analysis of 5G and 6G Communication Networks 93Madhusmita Mishra, Sarat Kumar Patra and Ashok Kumar Turuk4.1 Introduction 944.2 Performance Analysis of the Modified BSLM Technique Using PAPR Characteristics and Various Phase Sequences 964.2.1 Overview of SLM-Based PAPR Reduction and Modification 964.2.2 PAPR Reduction Analysis Using CCDF 1004.2.3 Analysis of PAPR Reduction Using Various Phase Sequences 1014.3 Mutual Independency Basing on Joint Cumulants 1084.4 Computational Complexity 1104.5 Conclusion 110References 1115 Leveraging 6G Networks for Disaster Monitoring and Management in Remote Sensing 115G. Vinuja and N. Bharatha Devi5.1 Introduction 1165.2 Literature Review 1185.2.1 Overview of 6G Networks and Their Potential Benefits in Disaster Management 1275.3 Real-Time Disaster Monitoring and Management Using Remote Technologies 1285.3.1 Enhanced Connectivity 1285.3.2 Remote Sensing and Monitoring 1285.3.3 Data Analytics and AI 1295.3.4 Virtual Reality (VR) and Augmented Reality (AR) 1295.3.5 Telemedicine and Remote Healthcare 1295.3.6 Public Awareness and Communication 1295.3.7 Smart Infrastructure and IoT Integration 1305.3.8 Quicker Response Times 1305.3.9 Enhanced Risk Assessment 1305.3.10 Resource Allocation Optimization 1305.3.11 Enhanced Coordination and Collaboration 1305.3.12 Targeted Recovery and Reconstruction 1315.3.13 Enhanced Preparedness and Planning 1315.4 Methodology 1315.4.1 Description of Research Design 1325.4.2 Data Collection Methods 1335.4.3 Analysis Techniques 1345.5 Results 1345.5.1 Summary of Data Collected 1355.5.2 Analysis of Data 1365.5.3 Discussion of Findings 1365.6 Discussion 1395.6.1 Interpretation of Results 1395.6.2 Implications for the Future of Disaster Management 1405.7 Conclusion 140References 1416 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring 145G. Vinuja and N. Bharatha Devi6.1 Introduction 1456.2 Literature Review 1496.3 Experimental Methods and Materials 1536.3.1 Fast Data Transfer and Processing 1536.3.2 Improved Accuracy and Precision in Monitoring 1546.3.3 Enhanced Data Security and Privacy 1556.4 Results and Discussion 1566.4.1 Innovative Remote Sensing Devices 1566.4.2 Real-Time Monitoring Using Smart Sensors 1576.4.3 Integration of 6G Technology and Artificial Intelligence 1596.5 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring 1596.5.1 Soil and Water Quality Monitoring 1606.5.2 Climate and Weather Monitoring 1606.5.3 Air Pollution Monitoring 1616.6 Challenges and Limitations of Implementing 6G Technology in Environmental Monitoring 1616.6.1 High Cost of Installation and Maintenance 1626.6.2 Lack of Trained Professionals in 6G Technology 1626.6.3 Ethical and Legal Concerns Surrounding Data Privacy 1636.7 Conclusion 163References 1647 Transforming Remote Sensing with Sixth-Generation Wireless Technology 169Bishnu Kant Shukla, Amit Tripathi, Ayushi Bhati, Vaishnavi Bansal, Pushpendra Kumar Sharma and Shivam Verma7.1 Introduction 1707.2 Understanding Remote Sensing 1717.2.1 Scattering and Absorption of EMR in Atmosphere 1717.2.2 Interaction of EMR with Target 1727.2.3 Spectral Signatures of Different Targets 1727.3 Sensor Technologies in Remote Sensing 1737.3.1 Passive and Active Sensors 1737.3.2 Hyperspectral and Multispectral Sensors 1737.3.3 Thermal Imaging 1747.3.4 Geostationary and Geosynchronous Satellites 1757.4 Resolution in Remote Sensing 1767.4.1 Spatial Resolution 1767.4.2 Spectral Resolution 1777.4.3 Temporal Resolution 1787.4.4 Radiometric Resolution 1787.5 Remote Sensing Techniques and Processing 1797.5.1 False Color Composite, True Color Composite 1797.5.2 Stereoscopy 1797.5.3 Along-Track Scanners, Across-Track Scanners 1807.5.4 Instantaneous Field of View (IFOV) 1807.5.5 Digital Image Processing 1817.6 Microwave Remote Sensing 1827.6.1 Radar 1837.6.2 Radar Shadow Effects, Layover Effects 1837.7 The Advent of 6G Technology 1847.7.1 Understanding 6G Technology 1847.7.2 Potential Impact of 6G on Remote Sensing 1857.8 Transforming Remote Sensing with 6G 1867.8.1 Improved Data Transfer and Processing 1867.8.2 Energy Efficiency in Remote Sensing Systems 1877.8.3 Increased Device Connectivity 1887.9 Case Studies: Application of 6G in Remote Sensing 1907.9.1 Agriculture: Crop Type Mapping, Crop Monitoring, and Damage Assessment 1907.9.2 Forestry: Species Identification and Typing, Burn Mapping 1927.9.3 Geology 1927.10 Conclusion 193References 1958 Deep Learning Models for Image Annotation Application in a 6G Network Environment 201Sandhya Avasthi, Suman Lata Tripathi, Tanushree Sanwal and Mufti Mahmud8.1 Introduction 2028.1.1 Image Detection and Annoation Applications 2038.1.2 How Do 6G Networks Enhance Image Annotation Performance? 2048.2 6G Network Overview 2058.2.1 5G Limitations 2068.2.2 Deep Learning with 6G 2078.3 Deep Learning Models for Image Annotation 2078.3.1 Convolution Neural Network (CNN) 2088.3.2 Recurrent Neural Network 2098.3.3 Long Short-Term Memory (LSTM) 2108.4 Automatic Image Annotation Framework in Real Time 2118.4.1 Deep Learning-Based Image Annotation Process Pipeline 2118.4.2 Preprocessing 2118.4.3 Feature Extraction 2128.4.4 Segmentation 2138.4.5 Object Detection 2148.4.6 Annotation or Labeling of Objects 2148.5 Challenges in Implementing Image Annotation Application 2148.6 6G and Transformation World Wide 2158.7 Challenges in 6G 2168.8 Conclusion 218References 2199 Integration of Artificial Intelligence in 6G Networks for Processing Blood Cancer Data 223R. Senthil Ganesh, S. A. Sivakumar and B. Maruthi Shankar9.1 Insights into 6G Networks: Revolutionizing Healthcare Data Processing 2249.2 Methodology for Blood Cancer Data Processing 2269.3 Enhancing Diagnostics, Treatment Planning, and Patient Monitoring Using 6G Networks 2289.4 Various AI Techniques for Analyzing Blood Cancer Data 2299.5 AI Integration in 6G Networks for Blood Cancer Data Processing 2309.6 Results and Discussions 2339.7 Conclusion 236References 23810 Enhancing Connectivity and Data-Driven Decision-Making for Smart Agriculture by Embracing 6G Technology 241Y.V.R. Naga Pawan and Kolla Bhanu Prakash10.1 Fundamental Concepts of Smart Agriculture 24210.1.1 Smart Agriculture 24210.2 Applications of 6G in SA 24310.3 Empowerment of 6G in SA 24910.4 Enhanced Monitoring and Predictive Analytics in SA 25010.4.1 Predictive Analytics 25210.5 Advantages of 6G in SA 25310.6 Challenges in the Implementation of 6G in SA 257References 26011 Security and Cost Optimization in Laser-Based Fencing Solutions 265Sanmukh Kaur and Anurupa Lubana11.1 Introduction 26511.2 Potential Security Challenges 26611.2.1 Beam Spoofing 26611.2.2 Beam Bending 26811.3 Objectives of the Chapter 26811.3.1 To Defend the Laser Fencing Against Potential Attacks 26811.3.2 To Optimize the Cost of Manufacturing and Operating 26811.4 Secure Communication Protocol 26911.4.1 Node Setup 26911.4.2 Protocol 27011.4.2.1 Packet Structure 27011.4.2.2 Fence State 27011.4.2.3 Seed and Encryption 27111.4.2.4 Timestamp Counter 27111.4.2.5 Error Checking 27111.5 Algorithm 27211.6 Conclusion 275References 27612 Security and Privacy in 6G-Based Human–Computer Interfaces: Challenges and Opportunities 277Kamaraj Arunachalam and Senthil Kumar Jagatheesaperumal12.1 Introduction 27812.2 Evolution of 6G Networks and HCIs 28012.2.1 Connected Robotics and Autonomous Systems 28112.2.2 Wireless Brain–Computer Interactions (BCIs) 28112.2.3 Haptic Communication and Smart Healthcare 28212.2.4 Automation and Industrial Ecosystem 28212.2.5 Internet of Everything (IoE) 28212.3 Risks and Vulnerabilities in 6G-Based HCIs 28312.4 Solutions and Strategies for Ensuring Security and Privacy 28612.4.1 Authentication Techniques in 6G HCIs 28612.4.2 Encryption Algorithms and Protocols 28712.4.3 Cybersecurity Measures for HCIs 28812.4.4 Privacy-Enhancing Technologies 28912.5 Future Trends and Opportunities for Enhancing Security and Privacy 29112.5.1 Advancements in User Identification and Authentication 29112.5.2 Secure Data Transmission and Storage 29212.5.3 Incorporating Privacy by Design 29312.5.4 Collaboration and Standardization Efforts 29312.6 Conclusion 294References 29413 Security and Privacy in 6G Applications: Optimization and Realization of Stochastic-Based Rapid Random Number Generation 299S. Nithya Devi, S. Senthil Kumar, V. K. Reshma and S. Shanmugaraju13.1 Introduction 30013.2 Literature Review 30213.3 Problem with Sensor Data 30413.4 Study Process 30513.4.1 Conventional Digital Clock Manager Scheme 30513.4.2 Stochastic Circuits 30713.4.3 Rapid Generating of Random Numbers Using a Stochastic Model 30713.4.4 Received Signal Strength Indicator (RSSI) 30913.4.5 Setting Up the Experiment and Collecting Data 31013.4.6 QCA Multiplexers and D-Latch 31013.5 Results and Analysis 31213.6 Conclusion 315References 31614 Roles and Challenges of 6G for the Human–Computer Interface 319Priyabrata Dash, Akankshya Patnaik, Sarat Kumar Sahoo and Franco Fernando Yanine14.1 Introduction 32014.2 Sixth Generation 32214.3 Roles of 6G for the Human–Computer Interface 32614.4 Challenges of 6G for the Human–Computer Interface 32814.5 Uses of 6G in Different Sectors 33114.6 Impact of 6G in Organizations 33314.7 Conclusion 334References 33515 Leveraging 6G Technology for Advancements in Smart Agriculture: Opportunities and Challenges 339B. Sathyasri, R.S. Valarmathi and G. Aloy Anuja Mary15.1 Introduction 34015.2 Literature Review 34515.3 Methodology 34515.3.1 Benefits of 6G in Smart Agriculture 34515.3.2 Increased Precision and Accuracy in Farming Practices 34615.3.3 Real-Time Monitoring and Data Collection 34615.3.4 Improved Communication and Collaboration Among Farmers 34715.3.5 Efficient Allocation of Resources 34715.3.6 Enhanced Crop Yields and Quality 34715.4 Challenges to Implementing 6G in Smart Agriculture 34815.4.1 High Cost of Technology 34815.4.2 Limited Network Coverage in Rural Areas 34915.4.3 Concerns over Data Security and Privacy 34915.4.4 Need for Technical Expertise to Operate and Maintain Technology 35015.5 Potential Applications of 6G in Smart Agriculture 35015.5.1 Crop Monitoring and Management 35115.5.2 Livestock Monitoring and Disease Control 35115.5.3 Smart Irrigation Systems 35215.5.4 Automated Machinery and Equipment 35215.5.5 Supply Chain Management 35315.6 Expected Outcomes 35315.7 Example of a Farm or Company That Has Successfully Adopted 6G Technology 35415.8 Benefits Experienced and Impact on Agricultural Productivity 35515.8.1 Lessons Learned and Recommendations for Others 35615.9 Conclusion 358References 35916 Exploring 6G Research: Advancements, Applications, and Challenges 363S. Senthil Kumar, S. Balaji, S. Nithya Devi and V. Priyadharsini16.1 Introduction 36416.2 Our Contributions and Comparable Work 36516.2.1 Previous Studies 36616.2.2 Contributions 36716.3 Credibility 36716.3.1 Reliability 36716.3.2 Security and Safety 36816.3.3 Dependability in 6G Networks 36816.4 Reliability, ML, and 6G 36816.4.1 Background in Brief 36916.4.2 Dependability of Federated Learning 36916.4.2.1 Reliability 37016.4.2.2 Availability 37116.4.2.3 Safety 37116.5 Dependability for Mission-Critical Applications 37116.5.1 Dependability Analysis of 6G MCAs 37216.5.2 Availability 37216.6 Future Research Directions 37216.7 Conclusions 374References 37417 E-Travel ID-Based Bus Fare Collection System Using 6G Networks 379S. A. Sivakumar, Pavithra K., Pavatharani P., Naviyarasu G. and Sajetha M.17.1 Insights into 6G Networks 38017.2 Impact of 6G on Transportation Sector 38117.3 Existing Approach and Problem Identification 38317.4 E-Travel ID-Based Bus Fare Collection System Using 6G Networks 38517.5 Results and Discussion 38817.6 Conclusion 392References 39318 Alert Generation Tool for Messaging Systems 395Akshaya K. and Sanmukh Kaur18.1 Introduction 39518.2 Importance of Alerts in the Messaging System 39618.2.1 System Health Monitoring 39618.2.2 Proactive Issue Resolution 39718.2.3 Performance Optimization 39718.2.4 Capacity Planning 39718.2.5 Security and Compliance 39718.3 Monitoring CPU Usage in Real Time 39818.3.1 Importance of CPU Usage Monitoring 39818.3.1.1 Identifying Performance Bottlenecks 39818.3.1.2 Diagnosing Performance Issues 39818.3.1.3 Optimizing Resource Allocation 39818.3.1.4 Proactive Issue Detection 39918.3.1.5 Capacity Planning and Scaling 39918.3.1.6 Resource Efficiency and Cost Optimization 39918.3.2 Methodology 39918.3.2.1 Importing the Necessary Libraries 39918.3.2.2 User Input for Process ID 39918.3.2.3 Defining the “warning()” Function 40018.3.2.4 Defining the “monitor()” Function 40018.3.2.5 Scheduling the Monitoring Tasks 40018.3.2.6 Running the Monitoring Loop 40118.3.2.7 Python Code 40118.3.3 Output 40318.3.4 Benefits of Real-Time CPU Usage Monitoring 40418.4 URL Tracking 40418.4.1 Methodology 40518.4.1.1 Python Code 40618.4.1.2 Output 40618.4.1.3 Python Code 40718.4.2 Output 40818.5 Automated Delivery Performance Monitoring 40918.5.1 Methodology 41018.5.1.1 Code 41218.5.2 Output 41318.5.3 Applications 41518.5.3.1 Marketing Campaigns 41518.5.3.2 Transactional Notifications 41518.5.3.3 Customer Support Systems 41518.5.3.4 System Alerts 41518.5.3.5 Performance Evaluation 41518.6 High Volume of Testing Message Alert 41618.6.1 Methodology 41618.6.1.1 Import Necessary Libraries 41618.6.1.2 Set Up Twilio and Email Credentials 41618.6.1.3 Establish a Connection to MySQL Database 41618.6.1.4 Create a Cursor Object and Execute a Query 41618.6.1.5 Fetch Data and Create a Pandas DataFrame 41718.6.1.6 Export Data to Excel 41718.6.1.7 Count the Number of Testing Messages 41718.6.1.8 Close the Cursor and Connection 41718.6.1.9 Print Status Messages 41718.6.1.10 Send SMS and Email Notifications 41718.6.1.11 Python Code 41818.6.2 Output 41918.7 Conclusion 421References 42119 Design of an Underwater Robotic Fish Controlled through a Mobile Phone 423Mohammed Nisam, N. Mouli Sharm, Vajid N. O., Sobhit Saxena and Suman Lata Tripathi19.1 Introduction 42319.1.1 Block Diagram 42519.1.2 Flowchart and Explanation 42719.2 Module Code Description 42719.3 Description of Proposed Robotic Fish 42919.4 Component and Material Selection 43019.5 Conclusion 43519.6 Suggestion for Future Work 435References 436About the Editors 439Index 441

Suman Lata Tripathi, PhD, is a professor at Lovely Professional University, Phagwara, India with more than seventeen years of experience in academics. She has published more than 74 research papers in refereed science journals and conferences, 14 books, and a book series in addition to 13 Indian patents and four copyrights. She has organized several workshops, summer internships, and expert lectures for students and has worked as a session chair, conference steering committee member, editorial board member, and peer reviewer in national and international journals and conferences. Mufti Mahmud, PhD, is an associate professor of Cognitive Computing in the Department of Computer Science at Nottingham Trent University, United Kingdom. Additionally, he serves as the coordinator of the Computer Science and Informatics Unit of Assessment in the Research Excellence Framework at the university and the deputy group leader of and the Cognitive Computing and Brain Informatics research group. He is also an active member of the Computing and Informatics Research Centre and Medical Technologies Innovation Facility. He is a fellow of the Higher Education Academy, a senior member of the Institute of Electrical and Electronics Engineering, the Association of Computing Machinery, and a professional member of the British Computer Society. C. Narmadha, PhD, is an associate professor and head of the Department of Electronics and Communication Engineering at the Periyar Maniammai Institute of Science and Technology and University, India. She has more than 19 years of academic experience and has published more than 15 research articles. Additionally, she has organized several workshops, summer internships, expert lectures, and research and innovation activities for students. She is a professional associated as a member of the Indian Society for Technical Education, Computer Society of India, and Broadcast Engineering Society. Her areas of expertise include wireless sensor networks, computer and communication systems, and signal processing. S. Albert Alexander, PhD, is the recipient of the prestigious Raman Research Fellowship from the University Grants Commission from the Government of India. He has 15 years of academic and research experience, has published 57 technical papers in national and international journals, and presented 53 papers at national and international conferences. Additionally, he has completed four Government of India-funded projects and is currently working on an additional four research projects. He is a member and in prestigious positions in various national and international forums and has been invited as a speaker for over 250 programs across India and the United States.

• Preface xxiAcknowledgements xxiii1 Introduction to AI Techniques for 6G Application 1Manoj Singh Adhikari, Raju Patel, Manoj Sindhwani and Shippu Sachdeva1.1 Introduction 21.2 Different Generation of Communication: From 1G to 6G 41.2.1 First Generation (1G) 41.2.2 Second Generation (2G) 51.2.3 Third Generation (3G) 51.2.4 Fourth Generation (4G) 51.2.5 Fifth Generation (5G) 51.2.6 Sixth Generation (6G) 51.3 Key Features and Requirements of 6G Networks 61.3.1 Faster Data Speeds 61.3.2 Ultra-Low Latency 61.3.3 Massive Capacity 71.3.4 Energy Efficiency 71.3.5 Seamless Connectivity 71.3.6 Advanced Spectrum Management 71.3.7 Enhanced Security and Privacy 71.3.8 Artificial Intelligence Integration 71.3.9 Heterogeneous Network Architecture 81.4 Role of Artificial Intelligence in 6G 81.4.1 Intelligent Radio Resource Management 91.4.2 Beamforming and MIMO 91.4.3 Intelligent Network Slicing 91.4.4 Intelligent Edge Computing 91.4.5 Intelligent Internet of Things 91.4.6 Enhanced Privacy 101.4.7 Intelligent Network Organization 101.4.8 Intelligent User Experience and Services 101.5 Machine Learning for 6G Networks 101.5.1 Intelligent Resource Management 111.5.2 Dynamic Spectrum Access 111.5.3 Intelligent Beamforming 111.5.4 Network Anomaly Detection 111.5.5 Intelligent Edge Computing 111.5.6 Intelligent Internet of Things 121.5.7 Intelligent Network Slicing 121.5.8 Intelligent Network Planning and Optimization 121.5.9 Predictive Maintenance 121.6 Deep Learning for 6G Applications 121.6.1 Enhanced Communication Systems 131.6.2 Intelligent Beamforming and Antenna Systems 131.6.3 Image and Video Processing 131.6.4 Intelligent Internet of Things 131.6.5 Autonomous Systems 131.6.6 Natural Language Processing and Speech Recognition 141.6.7 Augmented Reality and Virtual Reality 141.6.8 Network Security 141.7 Edge Computing and AI in 6G 141.7.1 Distributed Intelligence 141.7.2 Low-Latency Applications 151.7.3 Intelligent Edge Devices 151.7.4 Edge-AI-Assisted Network Management 151.7.5 Federated Learning 151.7.6 AI-Driven Security 151.7.7 Edge-AI for Content Delivery 161.7.8 Context-Aware Applications 161.8 AI-Enhanced Network Security in 6G 161.8.1 Threat Detection and Prevention 161.8.2 Anomaly Detection 171.8.3 Intrusion Detection and Prevention Systems (IDPS) 171.8.4 User Authentication 171.8.5 AI-Enabled Threat Intelligence 171.8.6 Automated Security Incident Response 171.8.7 AI-Enhanced Security Analytics 181.8.8 Privacy-Preserving Techniques 181.9 Quantum Computing and AI Fusion in 6G 181.9.1 Enhanced AI Algorithms 181.9.2 Optimization and Search Problems 191.9.3 Security and Encryption 191.9.4 Quantum-Assisted Machine Learning 191.9.5 Quantum Sensor Networks 191.9.6 Quantum-Assisted Simulation 191.9.7 Quantum Machine Learning 201.9.8 Quantum-Assisted Optimization 201.10 AI for Smart City Applications in 6G 201.10.1 Intelligent Traffic Management 201.10.2 Energy Management and Sustainability 211.10.3 Smart Infrastructure Monitoring 211.10.4 Waste Management 211.10.5 Smart Public Security and Safety 211.10.6 AI-Enabled Citizen Services 211.10.7 Urban Planning and Design 221.10.8 Data Analytics and Insights 221.11 Challenges and Future Directions 221.11.1 Technical Complexity 221.11.1.1 Future Directions 231.11.2 Privacy and Security 231.11.2.1 Future Directions 231.11.3 Ethical Considerations 241.11.3.1 Future Directions 241.11.4 Infrastructure and Energy Efficiency 241.11.4.1 Future Directions 241.11.5 Collaboration and Standardization 241.11.5.1 Future Directions 251.11.6 Socioeconomic Impact 251.11.6.1 Future Directions 251.11.7 Environmental Sustainability 251.11.7.1 Future Directions 251.12 Conclusion 25References 262 AI Techniques for 6G Applications 29Jyoti R. Munavalli, Rashmi R. Deshpande and Jayashree M. Oli2.1 6G Communication 302.2 Artificial Intelligence (AI) Computing in 6G 342.3 Role of AI in 6G 372.4 AI Techniques for 6G 382.4.1 Supervised Learning 392.4.2 Unsupervised Learning 412.4.3 Reinforcement Learning 422.4.4 Federated Learning 442.4.5 Deep Learning 462.5 Use Cases/Applications 472.5.1 Holographic Applications 472.5.2 Ubiquitous Computing 482.5.3 Deep Sensing/Tactile Internet 502.5.4 Dynamic Spectrum Allocation 512.6 Conclusion 53References 533 An Evaluation of Pervasive Computing Using Narrowband Technology: Exploring the Implications for 5G and Future Generations 57Sriharipriya K. C., Athira Soman Nair, Kannanpuzha Chelsea Antony, Megha Nair B. and Amala Ipe3.1 Introduction 583.2 Features 593.2.1 Power Consumption 593.2.2 Improved Coverage and Sensitivity with Low Latency 613.2.3 Transmission Mode 613.2.4 Resource of Spectrum 623.2.5 Mode of Working 623.2.6 Structure of Frame 643.2.7 Network of NB-IoT 643.2.8 Semi-Static Link Adaptation 663.2.9 Retransmission of Data 663.3 Basic Principles and Core Technologies of Narrowband 673.3.1 Theory of Analysis of Connection 673.3.2 Theory of Latency Survey 683.3.3 The Mechanism for Coverage Enhancement 693.3.4 Technology with Ultra-Low Power 703.3.5 Relationship of Coupling Between Signaling and Data 713.4 Correlation of Other Communication Technology with NB-IoT 723.4.1 With eMTC Technology 723.4.1.1 Coverage 743.4.1.2 Power Consumption 753.4.1.3 Connection Count 753.4.1.4 Voice Assistance 763.4.1.5 Mobility Management 763.4.1.6 Network Deployment’s Effect on the Current Network 763.4.1.7 Operative Mode 773.4.1.8 Combined Results 773.4.2 With More Wireless Network Methods 773.5 Applications 803.6 Security Needs 833.6.1 Perception Layer 843.6.2 Transmission Layers 853.6.3 Application Layer 863.7 Conclusion 87References 884 Cumulant-Based Performance Analysis of 5G and 6G Communication Networks 93Madhusmita Mishra, Sarat Kumar Patra and Ashok Kumar Turuk4.1 Introduction 944.2 Performance Analysis of the Modified BSLM Technique Using PAPR Characteristics and Various Phase Sequences 964.2.1 Overview of SLM-Based PAPR Reduction and Modification 964.2.2 PAPR Reduction Analysis Using CCDF 1004.2.3 Analysis of PAPR Reduction Using Various Phase Sequences 1014.3 Mutual Independency Basing on Joint Cumulants 1084.4 Computational Complexity 1104.5 Conclusion 110References 1115 Leveraging 6G Networks for Disaster Monitoring and Management in Remote Sensing 115G. Vinuja and N. Bharatha Devi5.1 Introduction 1165.2 Literature Review 1185.2.1 Overview of 6G Networks and Their Potential Benefits in Disaster Management 1275.3 Real-Time Disaster Monitoring and Management Using Remote Technologies 1285.3.1 Enhanced Connectivity 1285.3.2 Remote Sensing and Monitoring 1285.3.3 Data Analytics and AI 1295.3.4 Virtual Reality (VR) and Augmented Reality (AR) 1295.3.5 Telemedicine and Remote Healthcare 1295.3.6 Public Awareness and Communication 1295.3.7 Smart Infrastructure and IoT Integration 1305.3.8 Quicker Response Times 1305.3.9 Enhanced Risk Assessment 1305.3.10 Resource Allocation Optimization 1305.3.11 Enhanced Coordination and Collaboration 1305.3.12 Targeted Recovery and Reconstruction 1315.3.13 Enhanced Preparedness and Planning 1315.4 Methodology 1315.4.1 Description of Research Design 1325.4.2 Data Collection Methods 1335.4.3 Analysis Techniques 1345.5 Results 1345.5.1 Summary of Data Collected 1355.5.2 Analysis of Data 1365.5.3 Discussion of Findings 1365.6 Discussion 1395.6.1 Interpretation of Results 1395.6.2 Implications for the Future of Disaster Management 1405.7 Conclusion 140References 1416 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring 145G. Vinuja and N. Bharatha Devi6.1 Introduction 1456.2 Literature Review 1496.3 Experimental Methods and Materials 1536.3.1 Fast Data Transfer and Processing 1536.3.2 Improved Accuracy and Precision in Monitoring 1546.3.3 Enhanced Data Security and Privacy 1556.4 Results and Discussion 1566.4.1 Innovative Remote Sensing Devices 1566.4.2 Real-Time Monitoring Using Smart Sensors 1576.4.3 Integration of 6G Technology and Artificial Intelligence 1596.5 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring 1596.5.1 Soil and Water Quality Monitoring 1606.5.2 Climate and Weather Monitoring 1606.5.3 Air Pollution Monitoring 1616.6 Challenges and Limitations of Implementing 6G Technology in Environmental Monitoring 1616.6.1 High Cost of Installation and Maintenance 1626.6.2 Lack of Trained Professionals in 6G Technology 1626.6.3 Ethical and Legal Concerns Surrounding Data Privacy 1636.7 Conclusion 163References 1647 Transforming Remote Sensing with Sixth-Generation Wireless Technology 169Bishnu Kant Shukla, Amit Tripathi, Ayushi Bhati, Vaishnavi Bansal, Pushpendra Kumar Sharma and Shivam Verma7.1 Introduction 1707.2 Understanding Remote Sensing 1717.2.1 Scattering and Absorption of EMR in Atmosphere 1717.2.2 Interaction of EMR with Target 1727.2.3 Spectral Signatures of Different Targets 1727.3 Sensor Technologies in Remote Sensing 1737.3.1 Passive and Active Sensors 1737.3.2 Hyperspectral and Multispectral Sensors 1737.3.3 Thermal Imaging 1747.3.4 Geostationary and Geosynchronous Satellites 1757.4 Resolution in Remote Sensing 1767.4.1 Spatial Resolution 1767.4.2 Spectral Resolution 1777.4.3 Temporal Resolution 1787.4.4 Radiometric Resolution 1787.5 Remote Sensing Techniques and Processing 1797.5.1 False Color Composite, True Color Composite 1797.5.2 Stereoscopy 1797.5.3 Along-Track Scanners, Across-Track Scanners 1807.5.4 Instantaneous Field of View (IFOV) 1807.5.5 Digital Image Processing 1817.6 Microwave Remote Sensing 1827.6.1 Radar 1837.6.2 Radar Shadow Effects, Layover Effects 1837.7 The Advent of 6G Technology 1847.7.1 Understanding 6G Technology 1847.7.2 Potential Impact of 6G on Remote Sensing 1857.8 Transforming Remote Sensing with 6G 1867.8.1 Improved Data Transfer and Processing 1867.8.2 Energy Efficiency in Remote Sensing Systems 1877.8.3 Increased Device Connectivity 1887.9 Case Studies: Application of 6G in Remote Sensing 1907.9.1 Agriculture: Crop Type Mapping, Crop Monitoring, and Damage Assessment 1907.9.2 Forestry: Species Identification and Typing, Burn Mapping 1927.9.3 Geology 1927.10 Conclusion 193References 1958 Deep Learning Models for Image Annotation Application in a 6G Network Environment 201Sandhya Avasthi, Suman Lata Tripathi, Tanushree Sanwal and Mufti Mahmud8.1 Introduction 2028.1.1 Image Detection and Annoation Applications 2038.1.2 How Do 6G Networks Enhance Image Annotation Performance? 2048.2 6G Network Overview 2058.2.1 5G Limitations 2068.2.2 Deep Learning with 6G 2078.3 Deep Learning Models for Image Annotation 2078.3.1 Convolution Neural Network (CNN) 2088.3.2 Recurrent Neural Network 2098.3.3 Long Short-Term Memory (LSTM) 2108.4 Automatic Image Annotation Framework in Real Time 2118.4.1 Deep Learning-Based Image Annotation Process Pipeline 2118.4.2 Preprocessing 2118.4.3 Feature Extraction 2128.4.4 Segmentation 2138.4.5 Object Detection 2148.4.6 Annotation or Labeling of Objects 2148.5 Challenges in Implementing Image Annotation Application 2148.6 6G and Transformation World Wide 2158.7 Challenges in 6G 2168.8 Conclusion 218References 2199 Integration of Artificial Intelligence in 6G Networks for Processing Blood Cancer Data 223R. Senthil Ganesh, S. A. Sivakumar and B. Maruthi Shankar9.1 Insights into 6G Networks: Revolutionizing Healthcare Data Processing 2249.2 Methodology for Blood Cancer Data Processing 2269.3 Enhancing Diagnostics, Treatment Planning, and Patient Monitoring Using 6G Networks 2289.4 Various AI Techniques for Analyzing Blood Cancer Data 2299.5 AI Integration in 6G Networks for Blood Cancer Data Processing 2309.6 Results and Discussions 2339.7 Conclusion 236References 23810 Enhancing Connectivity and Data-Driven Decision-Making for Smart Agriculture by Embracing 6G Technology 241Y.V.R. Naga Pawan and Kolla Bhanu Prakash10.1 Fundamental Concepts of Smart Agriculture 24210.1.1 Smart Agriculture 24210.2 Applications of 6G in SA 24310.3 Empowerment of 6G in SA 24910.4 Enhanced Monitoring and Predictive Analytics in SA 25010.4.1 Predictive Analytics 25210.5 Advantages of 6G in SA 25310.6 Challenges in the Implementation of 6G in SA 257References 26011 Security and Cost Optimization in Laser-Based Fencing Solutions 265Sanmukh Kaur and Anurupa Lubana11.1 Introduction 26511.2 Potential Security Challenges 26611.2.1 Beam Spoofing 26611.2.2 Beam Bending 26811.3 Objectives of the Chapter 26811.3.1 To Defend the Laser Fencing Against Potential Attacks 26811.3.2 To Optimize the Cost of Manufacturing and Operating 26811.4 Secure Communication Protocol 26911.4.1 Node Setup 26911.4.2 Protocol 27011.4.2.1 Packet Structure 27011.4.2.2 Fence State 27011.4.2.3 Seed and Encryption 27111.4.2.4 Timestamp Counter 27111.4.2.5 Error Checking 27111.5 Algorithm 27211.6 Conclusion 275References 27612 Security and Privacy in 6G-Based Human–Computer Interfaces: Challenges and Opportunities 277Kamaraj Arunachalam and Senthil Kumar Jagatheesaperumal12.1 Introduction 27812.2 Evolution of 6G Networks and HCIs 28012.2.1 Connected Robotics and Autonomous Systems 28112.2.2 Wireless Brain–Computer Interactions (BCIs) 28112.2.3 Haptic Communication and Smart Healthcare 28212.2.4 Automation and Industrial Ecosystem 28212.2.5 Internet of Everything (IoE) 28212.3 Risks and Vulnerabilities in 6G-Based HCIs 28312.4 Solutions and Strategies for Ensuring Security and Privacy 28612.4.1 Authentication Techniques in 6G HCIs 28612.4.2 Encryption Algorithms and Protocols 28712.4.3 Cybersecurity Measures for HCIs 28812.4.4 Privacy-Enhancing Technologies 28912.5 Future Trends and Opportunities for Enhancing Security and Privacy 29112.5.1 Advancements in User Identification and Authentication 29112.5.2 Secure Data Transmission and Storage 29212.5.3 Incorporating Privacy by Design 29312.5.4 Collaboration and Standardization Efforts 29312.6 Conclusion 294References 29413 Security and Privacy in 6G Applications: Optimization and Realization of Stochastic-Based Rapid Random Number Generation 299S. Nithya Devi, S. Senthil Kumar, V. K. Reshma and S. Shanmugaraju13.1 Introduction 30013.2 Literature Review 30213.3 Problem with Sensor Data 30413.4 Study Process 30513.4.1 Conventional Digital Clock Manager Scheme 30513.4.2 Stochastic Circuits 30713.4.3 Rapid Generating of Random Numbers Using a Stochastic Model 30713.4.4 Received Signal Strength Indicator (RSSI) 30913.4.5 Setting Up the Experiment and Collecting Data 31013.4.6 QCA Multiplexers and D-Latch 31013.5 Results and Analysis 31213.6 Conclusion 315References 31614 Roles and Challenges of 6G for the Human–Computer Interface 319Priyabrata Dash, Akankshya Patnaik, Sarat Kumar Sahoo and Franco Fernando Yanine14.1 Introduction 32014.2 Sixth Generation 32214.3 Roles of 6G for the Human–Computer Interface 32614.4 Challenges of 6G for the Human–Computer Interface 32814.5 Uses of 6G in Different Sectors 33114.6 Impact of 6G in Organizations 33314.7 Conclusion 334References 33515 Leveraging 6G Technology for Advancements in Smart Agriculture: Opportunities and Challenges 339B. Sathyasri, R.S. Valarmathi and G. Aloy Anuja Mary15.1 Introduction 34015.2 Literature Review 34515.3 Methodology 34515.3.1 Benefits of 6G in Smart Agriculture 34515.3.2 Increased Precision and Accuracy in Farming Practices 34615.3.3 Real-Time Monitoring and Data Collection 34615.3.4 Improved Communication and Collaboration Among Farmers 34715.3.5 Efficient Allocation of Resources 34715.3.6 Enhanced Crop Yields and Quality 34715.4 Challenges to Implementing 6G in Smart Agriculture 34815.4.1 High Cost of Technology 34815.4.2 Limited Network Coverage in Rural Areas 34915.4.3 Concerns over Data Security and Privacy 34915.4.4 Need for Technical Expertise to Operate and Maintain Technology 35015.5 Potential Applications of 6G in Smart Agriculture 35015.5.1 Crop Monitoring and Management 35115.5.2 Livestock Monitoring and Disease Control 35115.5.3 Smart Irrigation Systems 35215.5.4 Automated Machinery and Equipment 35215.5.5 Supply Chain Management 35315.6 Expected Outcomes 35315.7 Example of a Farm or Company That Has Successfully Adopted 6G Technology 35415.8 Benefits Experienced and Impact on Agricultural Productivity 35515.8.1 Lessons Learned and Recommendations for Others 35615.9 Conclusion 358References 35916 Exploring 6G Research: Advancements, Applications, and Challenges 363S. Senthil Kumar, S. Balaji, S. Nithya Devi and V. Priyadharsini16.1 Introduction 36416.2 Our Contributions and Comparable Work 36516.2.1 Previous Studies 36616.2.2 Contributions 36716.3 Credibility 36716.3.1 Reliability 36716.3.2 Security and Safety 36816.3.3 Dependability in 6G Networks 36816.4 Reliability, ML, and 6G 36816.4.1 Background in Brief 36916.4.2 Dependability of Federated Learning 36916.4.2.1 Reliability 37016.4.2.2 Availability 37116.4.2.3 Safety 37116.5 Dependability for Mission-Critical Applications 37116.5.1 Dependability Analysis of 6G MCAs 37216.5.2 Availability 37216.6 Future Research Directions 37216.7 Conclusions 374References 37417 E-Travel ID-Based Bus Fare Collection System Using 6G Networks 379S. A. Sivakumar, Pavithra K., Pavatharani P., Naviyarasu G. and Sajetha M.17.1 Insights into 6G Networks 38017.2 Impact of 6G on Transportation Sector 38117.3 Existing Approach and Problem Identification 38317.4 E-Travel ID-Based Bus Fare Collection System Using 6G Networks 38517.5 Results and Discussion 38817.6 Conclusion 392References 39318 Alert Generation Tool for Messaging Systems 395Akshaya K. and Sanmukh Kaur18.1 Introduction 39518.2 Importance of Alerts in the Messaging System 39618.2.1 System Health Monitoring 39618.2.2 Proactive Issue Resolution 39718.2.3 Performance Optimization 39718.2.4 Capacity Planning 39718.2.5 Security and Compliance 39718.3 Monitoring CPU Usage in Real Time 39818.3.1 Importance of CPU Usage Monitoring 39818.3.1.1 Identifying Performance Bottlenecks 39818.3.1.2 Diagnosing Performance Issues 39818.3.1.3 Optimizing Resource Allocation 39818.3.1.4 Proactive Issue Detection 39918.3.1.5 Capacity Planning and Scaling 39918.3.1.6 Resource Efficiency and Cost Optimization 39918.3.2 Methodology 39918.3.2.1 Importing the Necessary Libraries 39918.3.2.2 User Input for Process ID 39918.3.2.3 Defining the “warning()” Function 40018.3.2.4 Defining the “monitor()” Function 40018.3.2.5 Scheduling the Monitoring Tasks 40018.3.2.6 Running the Monitoring Loop 40118.3.2.7 Python Code 40118.3.3 Output 40318.3.4 Benefits of Real-Time CPU Usage Monitoring 40418.4 URL Tracking 40418.4.1 Methodology 40518.4.1.1 Python Code 40618.4.1.2 Output 40618.4.1.3 Python Code 40718.4.2 Output 40818.5 Automated Delivery Performance Monitoring 40918.5.1 Methodology 41018.5.1.1 Code 41218.5.2 Output 41318.5.3 Applications 41518.5.3.1 Marketing Campaigns 41518.5.3.2 Transactional Notifications 41518.5.3.3 Customer Support Systems 41518.5.3.4 System Alerts 41518.5.3.5 Performance Evaluation 41518.6 High Volume of Testing Message Alert 41618.6.1 Methodology 41618.6.1.1 Import Necessary Libraries 41618.6.1.2 Set Up Twilio and Email Credentials 41618.6.1.3 Establish a Connection to MySQL Database 41618.6.1.4 Create a Cursor Object and Execute a Query 41618.6.1.5 Fetch Data and Create a Pandas DataFrame 41718.6.1.6 Export Data to Excel 41718.6.1.7 Count the Number of Testing Messages 41718.6.1.8 Close the Cursor and Connection 41718.6.1.9 Print Status Messages 41718.6.1.10 Send SMS and Email Notifications 41718.6.1.11 Python Code 41818.6.2 Output 41918.7 Conclusion 421References 42119 Design of an Underwater Robotic Fish Controlled through a Mobile Phone 423Mohammed Nisam, N. Mouli Sharm, Vajid N. O., Sobhit Saxena and Suman Lata Tripathi19.1 Introduction 42319.1.1 Block Diagram 42519.1.2 Flowchart and Explanation 42719.2 Module Code Description 42719.3 Description of Proposed Robotic Fish 42919.4 Component and Material Selection 43019.5 Conclusion 43519.6 Suggestion for Future Work 435References 436About the Editors 439Index 441